Traveling companions can be discovered from the trajectories of moving objects. An object's trajectory is a sequence of its location points sorted chronologically. Traveling companions are thus moving objects with trajectories that are both spatially and temporally close. Existing methods for mining traveling companions can be broadly divided into two categories: methods based on pattern mining [1]}, [2]}, [3]} and methods based on representation learning [4]}, [5]}, [6]}. Pattern mining based methods often first define a particular movement pattern pertaining to traveling companions based on some similarity measurement of their trajectories, and then develop specific algorithms to extract the predefined patterns. The similarity measurements are often based on point-wise Euclidean distance and thereby require trajectories are aligned along timestamp. However, real trajectories often contain many missing location points and have to be interpolated, which introduces additional measurement errors. Representation learning based methods, on the other hand, do not rely on pattern definition or point-wise comparison. Instead, they learn the representations of the trajectories using machine/deep learning models and then cluster the representations to discover similar ones, which in turn represent similar trajectories. Existing models either require certain amount of feature engineering, or specific labels for supervised learning. But in practice, feature engineering is often problem dependent and time consuming, which introduces extra overhead to representation learning itself. Yet trajectory labels are usually unavailable as they are difficult to collect and often have ethics issues. Moreover, existing models focus more on learning spatial proximity between trajectories, therefore they can only extract objects with similar spatial features, which are not necessarily traveling companions.
